图像修复和关键字段对齐,用于二次打印文档图像中的错位重叠文本
Senlong Wang1, Junchao Ge1, Jiantao Zhang1
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.
Frontiers in artificial intelligence
|September 17, 2025
概括
本研究引入了一种新的预处理方法,以改善对文本重叠和不对齐的文档的光学字符识别 (OCR). 文本重叠恢复网络 (TORNet) 和关键字段对齐显著提高了工业应用的识别精度.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 工业文件处理面临着二次印刷信息的挑战.
- 文本的错位和与背景/表的重叠妨碍了光学字符识别 (OCR) 的准确性.
- 现有的OCR模型难以处理复杂的文档布局.
研究的目的:
- 开发一种有效的预处理方法,用于二次打印文档上的OCR.
- 解决工业文档识别中的文本错位和重叠问题.
- 提高从具有挑战性的文件中提取信息的准确性和效率.
主要方法:
- 引入一个文本重叠恢复网络 (TORNet) 用于图像恢复.
- 实施关键字领域对齐方法,以实现精确的结构纠正.
- 为OCR增强开发一个全面的预处理框架.
主要成果:
- 在TORNet中,前景 (0.17 dB) 和背景 (0.12 dB) 文本恢复的PSNR值得到了改进.
- 该框架实现了识别精度提高23%,场匹配精度提高31%.
- 与现有的商业OCR模型相比,在不对齐和重叠的文档上显著提高了性能.
结论:
- 拟议的预处理框架有效地解决了二次打印文档中的文本重叠和错位问题.
- 这种解决方案为工业环境中的OCR任务提供了显著的改进.
- 该研究提供了一种可靠的方法,可以从复杂的文档类型中准确地提取信息.
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